Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms
Authors: Xueru Zhang, Mohammad Mahdi Khalili, Mingyan Liu
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Numerical Experiments We use the same dataset as (Zhang & Zhu, 2017), i.e., the Adult dataset from the UCI Machine Learning Repository (Lichman, 2013). It consists of personal information of around 48,842 individuals... Figures 2(a)-2(b) show both Lmean(t) and Lrange(t) as vertical bars centered at Lmean(t). Their corresponding privacy upper bound is given in Figures 2(c)-2(d). |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan, USA. |
| Pseudocode | Yes | Algorithm 1 Penalty perturbation (PP) method Parameter: Determine θ such that 2c1 < Bi N + 2θVi) holds for all i. Initialize: Generate fi(0) randomly and λi(0) = 0d 1 for every node i N , t = 0 Input: {Di}N i=1, {αi(1), , αi(T)}N i=1 for t = 0 to T 1 do... |
| Open Source Code | No | The paper does not provide any specific links to source code repositories, nor does it explicitly state that the code is publicly available. |
| Open Datasets | Yes | We use the same dataset as (Zhang & Zhu, 2017), i.e., the Adult dataset from the UCI Machine Learning Repository (Lichman, 2013). |
| Dataset Splits | No | The paper uses the Adult dataset and mentions a "training set" and "test set" implicitly through evaluation metrics, but does not explicitly specify a "validation" set or detailed dataset splits (e.g., percentages or counts) for reproduction. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used (e.g., GPU models, CPU types, or cloud infrastructure details) for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We will use as loss function the logistic loss L (z) = log(1 + exp( z)), with |L | 1 and L c1 = 1 4. The regularizer is R(fi) = 1 2||fi||2 2. ... for simplicity of presentation we shall fix θ = 0.5, let ηi(t) = η(t) = θqt 1 1 , and noise αi(t) = α(t) = α(1)qt 1 2 for all nodes. |